Compute the metrics

1. After launching Chloe you will see this page (Step 1). You want to click on “selected” under “landscape metrics” in “treatment”.
   

   Step 1

2. On this new page, you want to load you occupancy map in ASCII format. For that you click on “browse”, and select the corresponding file.
   

   Step 2

3. Now you want to add the scale to include in your analysis, select a distance and click on “add” continue the process until you have all the scales you want to analyse.
   

   Step 3

4. The next step consist in adding the CSV where the coordinates of your sampling points are saved. For that you click on “browse”, and select the corresponding file.
   

   Step 4

5. Now you can add the metrics wanted for the analysis. You can check what they compute by clicking the help button at the bottom right in the Chloe window.
   

   Step 5

6. In this step you want to select a folder where the results will be saved. For that click on “browse” and select a folder.
   

   Step 6

7. For this analysis you want to upload only the CSV. If you choose to also export ASCII you will a create a ASCII map for each pair of selected scales and metrics.
   

   Step 7

8. This step is important for the rest of the analysis on R. You want to export all of your parameters in a file. For that click on the blue “export” button at the bottom of the page, and exported this “proprieties” file.
   

   Step 8

9. You can now click on the blue “run” button at the bottom right of the Chloe window to start your analysis.
   

   Step 9

Compute the metrics to test their correlations

To test the correlations across metrics two solutions are offered:

• If your sampling units are distributed randomly across all cover types you want to computed the metrics across all the landscape.

• If your sampling units are distributed uniformly in only one type of cover (e.g. the sampling was made only in wood), then you want to generate a random number of points in this cover to computed metrics.

You probably choosed a lot of metrics and scales to test. The analysis will become a lot more heaver for Chloe to handle. So now we will start to use Chloe in R to computed metrics.
You will also need the package “Multi”.

install.packages("devtools",repos = "http://cran.us.r-project.org")
library(devtools)
install_github("Pintademijote/multipack")

install.packages("FactoMineR",repos = "http://cran.us.r-project.org")

library(Multi)

source("C:/Users/Public/Inra/Chloe/R/chloe.R")
cl <- Chloe()

scales=read.Chloe.properties("C:/Users/pglem/Documents/Master/Stage M2/Données/batch_test3.properties","Distance") 
dist=as.numeric(scales)
metrics=read.Chloe.properties("C:/Users/pglem/Documents/Master/Stage M2/Données/batch_test3.properties","Metrics")

cl@input_ascii <-"C:/Users/pglem/Documents/Master/Stage M2/Données/ZAAR_dataSIG/cover2016/cover_bois_2016.asc"
cl@metrics <- metrics
cl@output_folder <- "C:/Users/pglem/Documents/Master/Stage M2/Données/Results_18_02/Sliding/"
cl@prefix <- "correlation_test_1"
cl@treatment <- "sliding"
cl@export_ascii <- TRUE
cl@delta_displacement<-10

pb <- txtProgressBar(min = 0, max = length(dist), style = 3)
repet=0
for (i in dist) {
  repet=repet+1
  setTxtProgressBar(pb, repet)
  cl@window_sizes <- c(i)
  run(cl)
}